Modelling and prediction of dry matter yield of perennial ryegrass cultivars sown in multi-environment multi-harvest trials in south-eastern Australia

Giri, Khageswor; Chia, Kohleth; Chandra, Subhash; Smith, K. F.; Leddin, Clare M.; Ho, Christie K.M.; Jacobs, J. L.

doi:10.1016/j.fcr.2019.107614

Cited by 14 publications

(14 citation statements)

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“…However, if enough resources are available, we recommend using results from fully replicated data which provide the greatest precision and accuracy. The results of this study also demonstrate the potential for a composite data modelling approach to be applied in cultivar evaluation trials, such as those that underpin the FVI [5,6], as this system is based on the ranking of cultivars. In the case of perennial species, such as perennial ryegrass, this method could be applied across years as well as in multiple harvests.…”

Section: Discussionmentioning

confidence: 72%

“…A more cost-effective method of testing the nutritive characteristics in pasture cultivar evaluation is likely to increase the availability of such information for dairy farmers and enable them to make more informed decisions on which cultivars to sow when this information becomes available through tools such as the forage value index (FVI) [5][6][7]. The recently developed FVI for south-eastern Australia [5,6] provides dairy farmers with an economic basis for perennial ryegrass cultivar selection. The index is based on trial data and estimated regionally based seasonal economic values of extra yield (kg DM/ha) [5].…”

Section: Introductionmentioning

confidence: 99%

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Application and Analysis of a Composite Sampling Strategy to Cost-Effectively Compare Nutritive Characteristics of Perennial Ryegrass Cultivars in Field Trials

2020

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Pasture nutritive value is economically important in south-eastern Australian dairy production systems, yet measurement of nutritive characteristics in pasture cultivar evaluation trials is not routinely undertaken, primarily due to cost. An approach aiming to reduce the total laboratory analysis costs in multi-harvest field trials by testing some entries as composite samples is provided. A field trial evaluating 31 trial entries sown in 4 replicates was used. On nine harvest occasions, samples were collected from each plot, dried, ground and analysed using near infrared spectroscopy for key nutritive characteristics (metabolisable energy (ME), crude protein (CP) and neutral detergent fibre (NDF)). Additionally, composite samples of 17 of the 31 entries from each harvest were created by combining sub-samples of material from each of four replicate plots into a single sample that was also analysed. A linear mixed model (LMM) analysis accounting for spatial and temporal variation as well as spatial and temporal correlations was conducted, comparing the full data model where all plots at all harvests were tested individually to a data model where some entries were evaluated as individual plots and others as composites. The precision and accuracy of the estimates for the two models were similar and best linear unbiased prediction (BLUP) means of the composite sampling strategy model were comparable to the full data model. It was concluded that if composite sampling is used in conjunction with testing samples from individual plots on a selection of cultivars, statistically valid inferences are possible and the total cost of determining the nutritive characteristics of perennial ryegrass cultivars in field trials can be reduced.

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Section: Discussionmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Application and Analysis of a Composite Sampling Strategy to Cost-Effectively Compare Nutritive Characteristics of Perennial Ryegrass Cultivars in Field Trials

2020

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“…In addition, the values of DIC indicated the model with residual variance as diagonal the best fit for all traits analyzed (Table 1). In perennial breeding better fitted models present, in general, heterogeneous residual variances structures, such as diagonal or unstructured (GIRI et al, 2019). For instance, spatial variation is recognized as the main disturbing factor for perennial crops and it may differ as a result of seasonal changes and crop growth stage (FAVERI et al, 2015;RESENDE, 2002).…”

Section: Resultsmentioning

confidence: 99%

Interrelations between productive, vegetative, and fruit quality traits in Tahiti acid lime through Bayesian inference

et al. 2022

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Although the fruit yield has a core importance in Tahiti acid lime breeding programs, other traits stand out among the quality fruit and vegetative traits as ones that still need to be improved in selection of superior genotypes. Appling efficient tools aiming selection, such as the Bayesian inference, becomes an alternative in perennial crops. This study applied Bayesian inference in the genetic evaluation of Tahiti acid lime genotypes and estimated the interrelation between vegetative, productive and fruit quality traits. Twenty-four acid lime genotypes were evaluated for number of fruits, fruit yield, canopy volume, stem diameter, soluble solids content, shell thickness, and juice yield traits. The genotypic values were estimated through Bayesian inference and models with different residual structure were tested via deviance information criterion. Pearson’s correlation and the path analysis were estimated, removing the multicollinearity effect. The Bayesian inference estimates genotypic values with high selective accuracy. The correlations obtained between traits from different groups can be useful in selection strategies for improvement of Tahiti acid lime. The Bayesian inference demonstrated to be an important tool and should be considered in perennial breeding programs.

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“…We assumed that errors were uncorrelated over time. If there are reasons to assume that the error, besides changing in size, is also correlated over time, this could be taken into account using an autoregressive or an ante-dependence model for the error (Zimmerman and Núñez-Antón, 2009; Funatogawa and Funatogawa, 2019; Giri et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Combining Crop Growth Modeling and Statistical Genetic Modeling to Evaluate Phenotyping Strategies

et al. 2019

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Genomic prediction of complex traits, say yield, benefits from including information on correlated component traits. Statistical criteria to decide which yield components to consider in the prediction model include the heritability of the component traits and their genetic correlation with yield. Not all component traits are easy to measure. Therefore, it may be attractive to include proxies to yield components, where these proxies are measured in (high-throughput) phenotyping platforms during the growing season. Using the Agricultural Production Systems Simulator (APSIM)-wheat cropping systems model, we simulated phenotypes for a wheat diversity panel segregating for a set of physiological parameters regulating phenology, biomass partitioning, and the ability to capture environmental resources. The distribution of the additive quantitative trait locus effects regulating the APSIM physiological parameters approximated the same distribution of quantitative trait locus effects on real phenotypic data for yield and heading date. We use the crop growth model APSIM-wheat to simulate phenotypes in three Australian environments with contrasting water deficit patterns. The APSIM output contained the dynamics of biomass and canopy cover, plus yield at the end of the growing season. Each water deficit pattern triggered different adaptive mechanisms and the impact of component traits differed between drought scenarios. We evaluated multiple phenotyping schedules by adding plot and measurement error to the dynamics of biomass and canopy cover. We used these trait dynamics to fit parametric models and P-splines to extract parameters with a larger heritability than the phenotypes at individual time points. We used those parameters in multi-trait prediction models for final yield. The combined use of crop growth models and multi-trait genomic prediction models provides a procedure to assess the efficiency of phenotyping strategies and compare methods to model trait dynamics. It also allows us to quantify the impact of yield components on yield prediction accuracy even in different environment types. In scenarios with mild or no water stress, yield prediction accuracy benefitted from including biomass and green canopy cover parameters. The advantage of the multi-trait model was smaller for the early-drought scenario, due to the reduced correlation between the secondary and the target trait. Therefore, multi-trait genomic prediction models for yield require scenario-specific correlated traits.

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Modelling and prediction of dry matter yield of perennial ryegrass cultivars sown in multi-environment multi-harvest trials in south-eastern Australia

Cited by 14 publications

References 7 publications

Application and Analysis of a Composite Sampling Strategy to Cost-Effectively Compare Nutritive Characteristics of Perennial Ryegrass Cultivars in Field Trials

Application and Analysis of a Composite Sampling Strategy to Cost-Effectively Compare Nutritive Characteristics of Perennial Ryegrass Cultivars in Field Trials

Interrelations between productive, vegetative, and fruit quality traits in Tahiti acid lime through Bayesian inference

Combining Crop Growth Modeling and Statistical Genetic Modeling to Evaluate Phenotyping Strategies

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